About me

I do machine learning and computer vision research, where I work with geometric models of observed data. The research tend to follow two directions: use geometric constructions to design models, or model constraints on data using geometry. Check the publications for more specific examples, and don't hesitate to get in touch.

News and Updates

August 22, 2019.
New preprint online.
Here David provide rather tight approximation bounds on the expected
length of a curve on a random manifold. Pretty exciting stuff as it
justifies an approximation we keep on using :-)

July 15, 2019.
I got sick and tired of typing
plt.plot(x.detach().cpu().numpy(), y.detach().cpu().numpy(), color="green"),
so I put together a little pyplot wrapper that converts
torch tensors automatically to numpy arrays. Here's
the code; let me know if you have smarter ways of getting the same result!

July 11, 2019.
Gave a talk about the stuff Only Bayes can do
at AIP 2019. Interesting meeting;
always great to make friends in new communities :-)

April 3, 2019.
Just returned from a great Villum
meeting on equality, diversity and inclusiveness in science.
Having a four year old daughter creates a bit of a ticking clock for
me on this topic... scary!

January 2, 2019.
Returned to the office after an offline-holiday to see two papers accepted
at AISTATS. Yay! Excellent work by
Georgios and
Anton.
I'll post the camera-ready papers when they're ready :-)

November 13, 2018.
Visited Prowler.io to see their fantastic
work. Also talked about the stuff that
Only Bayes can do.
Great fun!

October 24, 2018.
The ERC just posted a
short video interview with me
on the key issues of ML. The actual interview was repeatedly
interupted by people shaking cow bells (that's a Davos thing);
hopefully, you can't see that in the final take :-)

October 8, 2018.
Last week I had a one-week-intern from 9th grade in the lab; made him
solve linear regression with grid search. Interesting reminder that
people who are not familiar with derivatives would never think of
minimizing sum-of-squares (he naturally arrived at sum-of-absolute-values)...

September 24, 2018.
I am back from the "Summer Davos" meeting in the World Economic Forum.
Quite intense.
I think the research community could learn quite a lot on how to
structure a meeting to focus on "discussion" rather than "presentation". More on this later...

August 24, 2018.
I talked about data augmentation and CPAB at our machine learning summer school.
Enter the dragon...

July 14, 2018.
The second installment of GiMLi is now over.
What a fantastic show -- thanks to all involved in making this happen!

June 14, 2018.New preprint online
(it's NIPS formatted, but hasn't been submitted)
on why uncertainty quantification is essential for manifold/representation
learning. That's at least the case, when you approach the problem from
a differential geometric viewpoint, but I expect that the underlying
message is generally true.

June 4, 2018.
I put up a short note
on the non-central Nakagami distribution as I couldn't find information
on this distribution elsewhere.

May 29, 2018.Anton
posted his work on wrapped GP-LVMs on manifolds to arXiv.
Its great to have some nonlinear (i.e. non-geodesic) tools for modeling on non-linear spaces!

May 14, 2018.
It seems I was nominated for teacher-of-the-year award. Thanks to whoever nominated me :-)

March 24, 2018.
I have uploaded the camera-ready
of our work on diffeomorphic statial transformer nets.
If you don't need the flexibility of CPAB, then at least take the matrix exponential
of your affine transformation matrix.

March 2, 2018.
I finally got around to uploading the abstract for my
Oberwolfach talk.
This contain no new results, but the derivations are more explicit
than what we have presented elsewhere.

February 19, 2018.
Nicki's paper on spatial transformer nets appears to be on the magic
list of accepted CVPR papers -- yay!
I'll link to the PDF once we have finished the camera-ready version.

December 21, 2016.
The Villum Foundation
has awarded me a Young Investigator
grant. Absolutely amazing, and very humbling! As a consequence,
I have several open positions at both PhD and post doc levels.

December 12, 2016.
Returned from NIPS to find a big
NVIDIA GPU on my desk.
Thanks to the NVIDIA crowd for this delightful gift!

December 6, 2016.Georgios and I
presented the
LAND paper
at NIPS. Huge crowd of fun and
interesting researchers!

August 23, 2016.
Had fun lecturing about metric learning at the
Advanced Topics in ML summer school.
Always fun when the audience almost cries out for a Riemannian approach...

August 12, 2016.
Our work on locally adaptive normal distributions
has been accepted for NIPS 2016. This is a wonderfully
simple way to build well-behaved nonparametric models. I'll link
to the camera-ready version when it's finished.

June 26, 2016.
Presented our Open Problems paper at
COLT.
Many interesting discussions followed -- clearly COLT is a very curious community.

June 26, 2016.GIMLI is over.
We had an amazing set of speakers, but
equally important we also had an amazing set of attendees. Great discussions!

June 16, 2016.
It seems I was given an Outstanding Reviewer Award from the
ICML 2016 program committee -- thanks!

June 10, 2016.Sofie,
myself, and
Lars
get our work on modeling forward models for EEG source reconstruction
accepted at NeuroImage.
Sometimes I'm amazed at how far you can get just using PCA...
I'll link to the paper as soon as possible.

June 9, 2016.Georgios,
Lars
and I finally put our work on locally adaptive normal distributions
online.
This is a really cool example of how Riemannian geometry is useful
for nonparametrics!

May 23, 2016.Aasa and I have an "Open Problems" paper at this years
COLT posing problems around the probability of seeing
a positive definite kernel matrix over geodesic spaces. Quite an intriguing
problem; do think about it :-)

May 2, 2016.
I finally put the final version of the
augmentation paper online.
This really is a fun example of how diffeomorphisms can be of great
use in machine learning. Hope to see you for the talk at
AISTATS!

February 5, 2016.
I use Sozi (the extension) together with
Inkscape for creating slides.
Sometimes it can be helpful to have a PDF version of the slides
rather than viewing the SVG file in your browser.
I've hacked together a naive script
for making this conversion -- it may be helpful to you as well.

January 12, 2016.
The PAMI version of the Grassmann Average paper is now
online.
More theory, more experiments, more pictures. Fun stuff!

December 28, 2015.
I've added some errata
(1,
2,
3,
4)
to a few papers. These are merely typos, but it's good to
document them when you find them -- let me know if you find more!

November 9, 2015.
My PAMI paper
on generalizing the classic principal curves to Riemannian manifolds
is now online. It's remarkable how well such a simple well-known algorithm
works compared to standard Riemannian models.

April 13, 2015.
We're currently on our way back from DALI 2015;
An extremely exciting small-scale meeting consisting of nothing but awesome
talks and discussions.

November 4, 2014.
Did you ever feel like designing a Gaussian kernel on a Riemannian manifold
using the intrinsic metric? Or perhaps on a more general metric space?
If so, you will want to tread lightly as this is generally not possible;
check out our recent work on
this if you're curious!

October 3, 2014.
I just noticed that my Youtube channel
has gotten more than one thousand views. Wow -- thanks for watching!

August 7, 2014.
The poster teaser video for our MICCAI 2014 paper is now online --
check it out!

August 7, 2014.
My talk at CVPR 2014 on Grassmann Averages is now available
at techtalks.tv -- enjoy!

July 2, 2014.
Posted a blog entry
about choices of subspace metrics for the
Grassmann
Average subspace estimator. Summary: for Gaussian data
most metrics gives the first principal component as the
subspace average.